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Proposal Preparation Data Quality Audit — Record Quality, String Normalisation & IQR Outlier Flags
x402
Proposal Preparation Data Quality Audit — Record Quality, String Normalisation & IQR Outlier Flags is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-24).
Runs three local analytical reports on proposal preparation records (completed_sections, planned_sections): record quality audit, string normalisation, and IQR-based outlier detection — up to 200 rows per call.
Price / call$0.01
Success—
StatusUnknown
How this service works
Proposal preparation — Record quality audit + String normalisation + IQR outlier flags: Record quality audit + String normalisation + IQR outlier flags for supplied proposal preparation records (completed_sections, planned_sections). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.
Output
Returns three independent analytical reports for the submitted proposal rows: (1) a record quality audit identifying missing values, format errors, and data integrity issues; (2) a string normalisation report flagging and correcting inconsistencies in text fields like document_group; (3) IQR-based outlier flags on the selected numeric field (completed_sections or planned_sections), identifying statistically extreme values. All analysis is performed locally with no external lookups.
Example use cases
Pre-submission proposal data cleanup
“Before I submit this proposal dataset, can you audit the record quality, normalise any string inconsistencies, and flag outliers in the completed_sections field? I have 12 rows across two document groups with dates ranging from 2026-09-01 to 2026-09-12.”
Outlier detection in planned sections
“I want to check if any of my planned_sections counts look statistically unusual — can you run the IQR outlier check and full quality audit on my 50-row proposal tracking dataset? The field to check is planned_sections.”
Monthly proposal tracking QA
“Run a quality audit on my monthly proposal progress records — I need the string normalisation report and IQR outlier flags for the completed_sections field across all 80 rows I'll send you, grouped by document_group.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-writing-08-203?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a fast, low-cost ($0.01 USDC) batch quality check on proposal preparation records specifically containing completed_sections and planned_sections fields. It is ideal for pre-analysis data validation pipelines where you want three combined reports (quality audit, string normalisation, outlier flags) in a single call without requiring live lookups or external integrations. Best suited for datasets up to 200 rows.
Side effects
- Exceeding 200 rows returns a validation error
- Missing required fields (row_id, date, completed_sections, planned_sections) causes rejection
- Invalid date format (not YYYY-MM-DD) causes schema validation failure
- Null or out-of-range numeric values (beyond ±1,000,000,000,000) may be rejected
- Empty rows array returns an error
- Selecting an invalid field enum value fails schema validation
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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